code: def setup_module():
      global gtab
  
      # generate a gradient table for phantom data
      directions8 = generate_bvecs(8)
      directions30 = generate_bvecs(30)
      directions60 = generate_bvecs(60)
      # Create full dataset parameters
      # (6 b-values = 0, 8 directions for b-value 300, 30 directions for b-value
      # 1000 and 60 directions for b-value 2000)
      bvals = np.hstack(
          (
              np.zeros(6),
              300 * np.ones(8),
              1000 * np.ones(30),
              2000 * np.ones(60),
          )
      )
      bvecs = np.vstack(
          (np.zeros((6, 3)), directions8, directions30, directions60)
      )
      gtab = gradient_table(bvals, bvecs)
  
imports: import warnings
  import numpy
  import scipy.special
  from numpy.testing import assert_, assert_equal, assert_raises, assert_array_almost_equal, assert_warns
  from dipy.denoise.localpca import dimensionality_problem_message, create_patch_radius_arr, compute_patch_size, compute_num_samples, compute_suggested_patch_radius, localpca, mppca, genpca, _pca_classifier
  from dipy.sims.voxel import multi_tensor
  from dipy.core.gradients import gradient_table, generate_bvecs
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 1 for calling a global function with example usage: setup_module() without return types